Can fear of failure predict academic procrastination? A study of Indonesian university students

This study aims to determine the ability of fear of failure in predicting academic procrastination in students in Indonesia by involving 428 respondents (male = 52.63%). The instruments used are the academic procrastination scale and the fear of failure scale. Analysis of this research data uses a simple linear regression technique. This study showed that fear of failure affected academic procrastination significantly with a contribution value of 38.6% positive direction, while 61.4% was influenced by other factors not studied. Research confirms that students with high levels of fear of failure tend to have high levels of academic procrastination and vice versa

Forecasting the onset date of southwest monsoon over Delhi

Using latest 32 years (1964-95) data, upper air temperatures and zonal and meridional components of winds of several selected stations for various standard isobaric levels (850 to 10 hPa) are screened for the pre-monsoon months of April & May in order to study their association with onset date of southwest monsoon at Delhi. Data for temperature and wind components for May for several stations exhibit significant correlations with onset-date. Some well known parameters presently in use in long range forecast models of monsoon seasonal rainfall have also been screened similarly. With a multiple regression technique, equations have been developed using suitable parameters from those which showed significant linear correlations.

Pengaruh Sikap, Norma Subjektif, Religiusitas, Pengetahuan Kewirausahaan, dan Faktor Demografi terhadap Minat Berwirausaha

This study aims to identify the influence of attitude variables, subjective norms, religiosity, entrepreneurial knowledge, and demographic factors on entrepreneurial interest. The population of this study were all active students of IAIN Salatiga. While the number of samples used were 374 students. Multiple linear regression technique was applied in this study to analyze the data. The results obtained from this study are simultaneously all independent variables have a significant effect on the interest in entrepreneurship. While partially, the variables that have a positive and significant effect on interest in entrepreneurship are attitudes, subjective norms, entrepreneurial knowledge, study programs, and entrepreneurial experience. While the variables of religiosity, gender, age, entrepreneurship courses, and campus organization partially have no significant effect. The advice that can be given based on this research is that the IAIN Salatiga institution should continue to encourage students to grow interest in entrepreneurship, these efforts can be made through strengthening entrepreneurship courses and entrepreneurial training.

Growth Rate Estimation of Rabi Pulse Production of Odisha by Using Spline Regression Technique

Pulses are considered to be important crop for ensuring nutritional security in Odisha. Proper estimation of growth rate in production of pulse crops allows for more effective cropping system planning and formulation of the agricultural policy of the state. To capture any abrupt changes and the variation in data in different phases of a long time period, spline regression technique is used as it can fit different models in different segments of the time period as necessary without losing the continuity of the model. The present study deals with the estimation of growth rate of area, yield and production of all rabi pulses in Odisha by using best fit spline regression model. To fit the spline regression model, the entire period of study is divided into different segments based on the scatter plot diagram which is further confirmed by testing the significance of change in coefficient of variation between the consecutive segments by chi square test. The regression model found to be suitable from the study of scatter plot of data are linear, compound, logarithmic, power, quadratic and cubic model. The best fit model is selected on the basis of error assumption test and model fit statistics such as R2, adjusted R2 and Mean Absolute Percentage error (MAPE). The respective selected best fit model is used for the estimation of growth rates of area, yield and production of rabi pulses in Odisha for each segment and the whole period of study. Among the spline regression models, the respective linear spline regression model is found to be best fit for area, yield and production of rabi pulses and are used for growth rate estimation of these variables. It is found that though the growth rate in area and yield of rabi pulses are not significant, the growth rate of production is found to be significant for the whole period of study which shows that the interaction effect of area and yield on production seems to dominate.

Comparison of Lasso and stepwise regression technique for wheat yield prediction

Multiple regression approach has been used to forecast the crop production widely. This study has been undertaken to evaluate the performance of stepwise and Lasso (Least absolute shrinkage and selection operator) regression technique in variable selection and development of wheat forecast model for crop yield using weather data and wheat yield for the period of 1984-2015, collected from IARI, New Delhi. Statistical parameters viz. R2, RMSE, and MAPE were 0.81, 195.90 and 4.54 per cent respectively with stepwise regression and 0.95, 99.27, 2.7 percentage, respectively with Lasso regression. Forecast models were validated during 2013-14 and 2014-15. Prediction errors were -8.5 and 10.14 per cent with stepwise and 1.89 and 1.64 percent with the Lasso. This shows that performance of Lasso regression is better than stepwise regression to some extent.

Power Estimation and Validation of Embedded Multiplier Based on ANN and Regression Technique

Nowadays, high-end Field-Programmable Gate Arrays (FPGAs) are capable of implementing relatively high-performance systems in the field of Digital Signal Processing (DSP). Due to the abundant application of multipliers, their implementation efficiency and performance have become a critical issue in designing the DSP systems. On the other hand, FPGAs consume a large amount of power due to their complex circuitry. So, the power estimation of FPGA implementations at an early design stage has become a critical design metric. Various models are available in the literature based on Look-up Tables (LUTs), but not much literature is available on speed-optimized multiplier design using DSP slices only. In this paper, an embedded multiplier (12.0 IP core) has been analyzed and customized for different Input/Output (I/O) configurations to estimate the power using Vivado Design Suite (2014.4) targeted to the Zynq-family FPGA device (Zynq evolution and development kit). The embedded multiplier IP has been optimized for performance using two different approaches, i.e., Mults (DSP)-based and LUTs-based. Post-synthesis attributes have been used for formulating the power estimation models based on Artificial Neural Network (ANN) and curve fitting and regression technique. The power values estimated from the proposed models have been authenticated with reference to those assessed from the commercial tool. Based on the results obtained, ANN-based model provides average errors of 0.73% and 0.88% for the LUTs and DSP-based designs, respectively. Whereas, the model based on curve fitting and regression technique provides average errors of 3.61% and 1.59% for the LUTs and DSP-based designs, respectively. The timing analysis has been done to get the design performance and time complexity of the proposed models. Area analysis of the design has also been performed in order to report the resource utilization.